• Spectroscopy and Spectral Analysis
  • Vol. 38, Issue 11, 3362 (2018)
WANG Hui-quan1、2、*, REN Li-na1, ZHAO Zhe1、2, WANG Jin-hai1、2, and CHEN Hong-li1、2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3964/j.issn.1000-0593(2018)11-3362-06 Cite this Article
    WANG Hui-quan, REN Li-na, ZHAO Zhe, WANG Jin-hai, CHEN Hong-li. The Study of Fast Localization Method of Anomaly Block in Tissue Based on Differential Optical Density[J]. Spectroscopy and Spectral Analysis, 2018, 38(11): 3362 Copy Citation Text show less

    Abstract

    The position of the source-detector (S-D) relative to the anomaly had an important influence on the detection effect when the detection of the anomaly in tissues was non-invasive based on near-infrared spectroscopy. In this study, a Single-Source Multi-Detectors structure was designed in order to realize the rapid localization of anomaly within the organization. This method was for finite element analysis of optical density distribution for different horizontal positions, depths and diameters of anomaly. Then calculated the difference in optical density between the detectors. The simulation results showed that the horizontal position of the anomaly in the tissue could be quickly located according to the differential optical density difference curves formed by the multiple detectors. The Gaussian fitting feature of these curves has a strong correlation with the horizontal positions, depths and diameters of the anomaly. Through the differential optical density difference curves, rapid localization could be achieved within the region of interest in the organization. It provides an important reference for the sources and detectors location in terms of tumor detection, brain function optical imaging and other fields using near infrared spectroscopy, which can improve its detection accuracy.
    WANG Hui-quan, REN Li-na, ZHAO Zhe, WANG Jin-hai, CHEN Hong-li. The Study of Fast Localization Method of Anomaly Block in Tissue Based on Differential Optical Density[J]. Spectroscopy and Spectral Analysis, 2018, 38(11): 3362
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